{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style ='darkgrid', font_scale=1.5)  #设置背景\n",
    "plt.rcParams['font.sans-serif'] =['SimHei'] #设置字体，支持中文\n",
    "plt.rcParams['axes.unicode_minus'] =False\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T08:22:42.251042500Z",
     "start_time": "2024-05-06T08:22:42.238449300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1、数据查看和预处理"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import os\n",
    "# 对于电脑比较弱（4G内存，cpu比较弱）就在这里选前100万条数据即可，nrows=1000000\n",
    "data_user = pd.read_csv('tianchi_mobile_recommend_train_user.csv',dtype = str)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T08:23:32.062136300Z",
     "start_time": "2024-05-06T08:23:18.260095800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12256906 entries, 0 to 12256905\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Dtype \n",
      "---  ------         ----- \n",
      " 0   user_id        object\n",
      " 1   item_id        object\n",
      " 2   behavior_type  object\n",
      " 3   user_geohash   object\n",
      " 4   item_category  object\n",
      " 5   time           object\n",
      "dtypes: object(6)\n",
      "memory usage: 561.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data_user.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T08:23:37.275740300Z",
     "start_time": "2024-05-06T08:23:37.220726100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "    user_id    item_id behavior_type user_geohash item_category           time\n0  98047837  232431562             1          NaN          4245  2014-12-06 02\n1  97726136  383583590             1          NaN          5894  2014-12-09 20\n2  98607707   64749712             1          NaN          2883  2014-12-18 11\n3  98662432  320593836             1      96nn52n          6562  2014-12-06 10\n4  98145908  290208520             1          NaN         13926  2014-12-16 21",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>98047837</td>\n      <td>232431562</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>4245</td>\n      <td>2014-12-06 02</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>97726136</td>\n      <td>383583590</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>5894</td>\n      <td>2014-12-09 20</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>98607707</td>\n      <td>64749712</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>2883</td>\n      <td>2014-12-18 11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>98662432</td>\n      <td>320593836</td>\n      <td>1</td>\n      <td>96nn52n</td>\n      <td>6562</td>\n      <td>2014-12-06 10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>98145908</td>\n      <td>290208520</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>13926</td>\n      <td>2014-12-16 21</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:44:01.693514800Z",
     "start_time": "2024-05-06T09:44:01.649634900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id          0.00000\nitem_id          0.00000\nbehavior_type    0.00000\nuser_geohash     0.68001\nitem_category    0.00000\ntime             0.00000\ndtype: float64"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 缺失值分析，apply默认对列进行操作，x代表一列\n",
    "data_user.apply(lambda x: sum(x.isnull())/len(x))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:44:15.210941100Z",
     "start_time": "2024-05-06T09:44:09.466864100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id                0\nitem_id                0\nbehavior_type          0\nuser_geohash     8334824\nitem_category          0\ntime                   0\ndtype: int64"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#统计多少条是空的\n",
    "data_user.apply(lambda x: sum(x.isnull()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:44:35.868228Z",
     "start_time": "2024-05-06T09:44:29.134926900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "(12256906, 6)"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:45:46.998987800Z",
     "start_time": "2024-05-06T09:45:46.982180Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 先对日期进行处理"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "# 把日期和小时分别取出来，从日期，小时两个角度分析购物行为\n",
    "data_user['date'] = data_user['time'].str[0:10]\n",
    "data_user['hour'] = data_user['time'].str[11:]  #如果发生溢出就分开执行"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:46:58.389961100Z",
     "start_time": "2024-05-06T09:46:52.310547300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id          object\nitem_id          object\nbehavior_type    object\nuser_geohash     object\nitem_category    object\ntime             object\ndate             object\nhour             object\ndtype: object"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:49:05.023817700Z",
     "start_time": "2024-05-06T09:49:05.010719800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "    user_id    item_id behavior_type user_geohash item_category  \\\n0  98047837  232431562             1          NaN          4245   \n1  97726136  383583590             1          NaN          5894   \n2  98607707   64749712             1          NaN          2883   \n3  98662432  320593836             1      96nn52n          6562   \n4  98145908  290208520             1          NaN         13926   \n\n            time        date hour  \n0  2014-12-06 02  2014-12-06   02  \n1  2014-12-09 20  2014-12-09   20  \n2  2014-12-18 11  2014-12-18   11  \n3  2014-12-06 10  2014-12-06   10  \n4  2014-12-16 21  2014-12-16   21  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>98047837</td>\n      <td>232431562</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>4245</td>\n      <td>2014-12-06 02</td>\n      <td>2014-12-06</td>\n      <td>02</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>97726136</td>\n      <td>383583590</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>5894</td>\n      <td>2014-12-09 20</td>\n      <td>2014-12-09</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>98607707</td>\n      <td>64749712</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>2883</td>\n      <td>2014-12-18 11</td>\n      <td>2014-12-18</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>98662432</td>\n      <td>320593836</td>\n      <td>1</td>\n      <td>96nn52n</td>\n      <td>6562</td>\n      <td>2014-12-06 10</td>\n      <td>2014-12-06</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>98145908</td>\n      <td>290208520</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>13926</td>\n      <td>2014-12-16 21</td>\n      <td>2014-12-16</td>\n      <td>21</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:49:08.332714300Z",
     "start_time": "2024-05-06T09:49:08.291681300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "#类型转换\n",
    "data_user['date'] = pd.to_datetime(data_user['date'])\n",
    "data_user['time']= pd.to_datetime(data_user['time'])\n",
    "data_user['hour'] = data_user['hour'].astype(np.int8) #节约内存"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:49:53.067158200Z",
     "start_time": "2024-05-06T09:49:51.137605Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id                  object\nitem_id                  object\nbehavior_type            object\nuser_geohash             object\nitem_category            object\ntime             datetime64[ns]\ndate             datetime64[ns]\nhour                       int8\ndtype: object"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:50:12.244027700Z",
     "start_time": "2024-05-06T09:50:12.224054800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "    user_id    item_id behavior_type user_geohash item_category  \\\n0  98047837  232431562             1          NaN          4245   \n1  97726136  383583590             1          NaN          5894   \n2  98607707   64749712             1          NaN          2883   \n3  98662432  320593836             1      96nn52n          6562   \n4  98145908  290208520             1          NaN         13926   \n\n                 time       date  hour  \n0 2014-12-06 02:00:00 2014-12-06     2  \n1 2014-12-09 20:00:00 2014-12-09    20  \n2 2014-12-18 11:00:00 2014-12-18    11  \n3 2014-12-06 10:00:00 2014-12-06    10  \n4 2014-12-16 21:00:00 2014-12-16    21  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>98047837</td>\n      <td>232431562</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>4245</td>\n      <td>2014-12-06 02:00:00</td>\n      <td>2014-12-06</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>97726136</td>\n      <td>383583590</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>5894</td>\n      <td>2014-12-09 20:00:00</td>\n      <td>2014-12-09</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>98607707</td>\n      <td>64749712</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>2883</td>\n      <td>2014-12-18 11:00:00</td>\n      <td>2014-12-18</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>98662432</td>\n      <td>320593836</td>\n      <td>1</td>\n      <td>96nn52n</td>\n      <td>6562</td>\n      <td>2014-12-06 10:00:00</td>\n      <td>2014-12-06</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>98145908</td>\n      <td>290208520</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>13926</td>\n      <td>2014-12-16 21:00:00</td>\n      <td>2014-12-16</td>\n      <td>21</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:50:14.172078500Z",
     "start_time": "2024-05-06T09:50:14.122988200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "#对数据进行时间排序，得到起始时间\n",
    "data_user.sort_values(by ='time',ascending=True,inplace = True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:51:54.735984300Z",
     "start_time": "2024-05-06T09:51:39.740907300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id    item_id behavior_type user_geohash item_category  \\\n1505077    73462715  378485233             1          NaN          9130   \n8686537    36090137  236748115             1          NaN         10523   \n4035788    40459733  155218177             1          NaN          8561   \n10113411     814199  149808524             1          NaN          9053   \n2936757   113309982    5730861             1          NaN          3783   \n\n               time       date  hour  \n1505077  2014-11-18 2014-11-18     0  \n8686537  2014-11-18 2014-11-18     0  \n4035788  2014-11-18 2014-11-18     0  \n10113411 2014-11-18 2014-11-18     0  \n2936757  2014-11-18 2014-11-18     0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1505077</th>\n      <td>73462715</td>\n      <td>378485233</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9130</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8686537</th>\n      <td>36090137</td>\n      <td>236748115</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>10523</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4035788</th>\n      <td>40459733</td>\n      <td>155218177</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>8561</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>10113411</th>\n      <td>814199</td>\n      <td>149808524</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9053</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2936757</th>\n      <td>113309982</td>\n      <td>5730861</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>3783</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:51:56.282932100Z",
     "start_time": "2024-05-06T09:51:56.205063600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id    item_id behavior_type user_geohash item_category  \\\n5241608   132653097  119946062             2          NaN          6054   \n10296029  130082553  296196819             1          NaN         11532   \n8527264    43592945  350594832             1      9rhhgph          9541   \n6263497    12833799  186993938             1      954g37v          3798   \n9200479    77522552   69292191             1          NaN           889   \n\n                        time       date  hour  \n5241608  2014-12-18 23:00:00 2014-12-18    23  \n10296029 2014-12-18 23:00:00 2014-12-18    23  \n8527264  2014-12-18 23:00:00 2014-12-18    23  \n6263497  2014-12-18 23:00:00 2014-12-18    23  \n9200479  2014-12-18 23:00:00 2014-12-18    23  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5241608</th>\n      <td>132653097</td>\n      <td>119946062</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>6054</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>10296029</th>\n      <td>130082553</td>\n      <td>296196819</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>11532</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>8527264</th>\n      <td>43592945</td>\n      <td>350594832</td>\n      <td>1</td>\n      <td>9rhhgph</td>\n      <td>9541</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>6263497</th>\n      <td>12833799</td>\n      <td>186993938</td>\n      <td>1</td>\n      <td>954g37v</td>\n      <td>3798</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>9200479</th>\n      <td>77522552</td>\n      <td>69292191</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>889</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.tail()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:52:22.268921600Z",
     "start_time": "2024-05-06T09:52:22.223691700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "#丢弃原有索引，按位置重新生成索引，在原有df生效\n",
    "data_user.reset_index(drop =True,inplace =True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:52:33.124897100Z",
     "start_time": "2024-05-06T09:52:33.089427100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "     user_id    item_id behavior_type user_geohash item_category       time  \\\n0   73462715  378485233             1          NaN          9130 2014-11-18   \n1   36090137  236748115             1          NaN         10523 2014-11-18   \n2   40459733  155218177             1          NaN          8561 2014-11-18   \n3     814199  149808524             1          NaN          9053 2014-11-18   \n4  113309982    5730861             1          NaN          3783 2014-11-18   \n\n        date  hour  \n0 2014-11-18     0  \n1 2014-11-18     0  \n2 2014-11-18     0  \n3 2014-11-18     0  \n4 2014-11-18     0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>73462715</td>\n      <td>378485233</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9130</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>36090137</td>\n      <td>236748115</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>10523</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>40459733</td>\n      <td>155218177</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>8561</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>814199</td>\n      <td>149808524</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9053</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>113309982</td>\n      <td>5730861</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>3783</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:52:35.663033600Z",
     "start_time": "2024-05-06T09:52:35.591101500Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 分析得出最火的单品、最火的品类、可能刷单的用户、最多的行为"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "         user_id    item_id behavior_type user_geohash item_category\ncount   12256906   12256906      12256906      3922082      12256906\nunique     10000    2876947             4       575458          8916\ntop     36233277  112921337             1      94ek6ke          1863\nfreq       31030       1445      11550581         1052        393247",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>3922082</td>\n      <td>12256906</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>10000</td>\n      <td>2876947</td>\n      <td>4</td>\n      <td>575458</td>\n      <td>8916</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>36233277</td>\n      <td>112921337</td>\n      <td>1</td>\n      <td>94ek6ke</td>\n      <td>1863</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>31030</td>\n      <td>1445</td>\n      <td>11550581</td>\n      <td>1052</td>\n      <td>393247</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top是出现最多次数的那个str值，freq是top的值出现的次数\n",
    "data_user.describe(include = ['object'])  #第二行可以看出行为类型，用户位置类别，商品品类"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:54:24.906599300Z",
     "start_time": "2024-05-06T09:54:11.045047500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "           user_id    item_id behavior_type user_geohash item_category  \\\n40360     36233277  147216340             1      94ek6lk          4721   \n40411     36233277  145482943             1      94ek6ln          7344   \n40526     36233277  302098857             1      94ek6kg          7344   \n40859     36233277  180885547             1      94ek6l1          4721   \n41017     36233277  113357921             1      94ek6as          4721   \n...            ...        ...           ...          ...           ...   \n12255805  36233277  260021102             1      94ek6ke          2825   \n12255823  36233277  312712899             1      94ek6ln          1863   \n12255850  36233277   34128930             1      94ek6ep          9835   \n12255932  36233277  116229820             1      94ek6lr          1863   \n12255938  36233277   81245809             1      94ek6kc          9541   \n\n                        time       date  hour  \n40360    2014-11-18 07:00:00 2014-11-18     7  \n40411    2014-11-18 07:00:00 2014-11-18     7  \n40526    2014-11-18 07:00:00 2014-11-18     7  \n40859    2014-11-18 07:00:00 2014-11-18     7  \n41017    2014-11-18 07:00:00 2014-11-18     7  \n...                      ...        ...   ...  \n12255805 2014-12-18 23:00:00 2014-12-18    23  \n12255823 2014-12-18 23:00:00 2014-12-18    23  \n12255850 2014-12-18 23:00:00 2014-12-18    23  \n12255932 2014-12-18 23:00:00 2014-12-18    23  \n12255938 2014-12-18 23:00:00 2014-12-18    23  \n\n[31030 rows x 8 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>40360</th>\n      <td>36233277</td>\n      <td>147216340</td>\n      <td>1</td>\n      <td>94ek6lk</td>\n      <td>4721</td>\n      <td>2014-11-18 07:00:00</td>\n      <td>2014-11-18</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>40411</th>\n      <td>36233277</td>\n      <td>145482943</td>\n      <td>1</td>\n      <td>94ek6ln</td>\n      <td>7344</td>\n      <td>2014-11-18 07:00:00</td>\n      <td>2014-11-18</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>40526</th>\n      <td>36233277</td>\n      <td>302098857</td>\n      <td>1</td>\n      <td>94ek6kg</td>\n      <td>7344</td>\n      <td>2014-11-18 07:00:00</td>\n      <td>2014-11-18</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>40859</th>\n      <td>36233277</td>\n      <td>180885547</td>\n      <td>1</td>\n      <td>94ek6l1</td>\n      <td>4721</td>\n      <td>2014-11-18 07:00:00</td>\n      <td>2014-11-18</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>41017</th>\n      <td>36233277</td>\n      <td>113357921</td>\n      <td>1</td>\n      <td>94ek6as</td>\n      <td>4721</td>\n      <td>2014-11-18 07:00:00</td>\n      <td>2014-11-18</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>12255805</th>\n      <td>36233277</td>\n      <td>260021102</td>\n      <td>1</td>\n      <td>94ek6ke</td>\n      <td>2825</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>12255823</th>\n      <td>36233277</td>\n      <td>312712899</td>\n      <td>1</td>\n      <td>94ek6ln</td>\n      <td>1863</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>12255850</th>\n      <td>36233277</td>\n      <td>34128930</td>\n      <td>1</td>\n      <td>94ek6ep</td>\n      <td>9835</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>12255932</th>\n      <td>36233277</td>\n      <td>116229820</td>\n      <td>1</td>\n      <td>94ek6lr</td>\n      <td>1863</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>12255938</th>\n      <td>36233277</td>\n      <td>81245809</td>\n      <td>1</td>\n      <td>94ek6kc</td>\n      <td>9541</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18</td>\n      <td>23</td>\n    </tr>\n  </tbody>\n</table>\n<p>31030 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#可能是刷单的用户\n",
    "data_user[data_user['user_id']=='36233277']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:54:55.090388200Z",
     "start_time": "2024-05-06T09:54:53.862797800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "         user_id    item_id behavior_type user_geohash item_category  \\\ncount   12256906   12256906      12256906      3922082      12256906   \nunique     10000    2876947             4       575458          8916   \ntop     36233277  112921337             1      94ek6ke          1863   \nfreq       31030       1445      11550581         1052        393247   \nmean         NaN        NaN           NaN          NaN           NaN   \nmin          NaN        NaN           NaN          NaN           NaN   \n25%          NaN        NaN           NaN          NaN           NaN   \n50%          NaN        NaN           NaN          NaN           NaN   \n75%          NaN        NaN           NaN          NaN           NaN   \nmax          NaN        NaN           NaN          NaN           NaN   \nstd          NaN        NaN           NaN          NaN           NaN   \n\n                                 time                           date  \\\ncount                        12256906                       12256906   \nunique                            NaN                            NaN   \ntop                               NaN                            NaN   \nfreq                              NaN                            NaN   \nmean    2014-12-04 04:47:28.445702144  2014-12-03 13:58:23.675396352   \nmin               2014-11-18 00:00:00            2014-11-18 00:00:00   \n25%               2014-11-26 15:00:00            2014-11-26 00:00:00   \n50%               2014-12-04 14:00:00            2014-12-04 00:00:00   \n75%               2014-12-11 23:00:00            2014-12-11 00:00:00   \nmax               2014-12-18 23:00:00            2014-12-18 00:00:00   \nstd                               NaN                            NaN   \n\n                hour  \ncount   1.225691e+07  \nunique           NaN  \ntop              NaN  \nfreq             NaN  \nmean    1.481799e+01  \nmin     0.000000e+00  \n25%     1.000000e+01  \n50%     1.600000e+01  \n75%     2.000000e+01  \nmax     2.300000e+01  \nstd     6.474778e+00  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>3922082</td>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>12256906</td>\n      <td>1.225691e+07</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>10000</td>\n      <td>2876947</td>\n      <td>4</td>\n      <td>575458</td>\n      <td>8916</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>36233277</td>\n      <td>112921337</td>\n      <td>1</td>\n      <td>94ek6ke</td>\n      <td>1863</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>31030</td>\n      <td>1445</td>\n      <td>11550581</td>\n      <td>1052</td>\n      <td>393247</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2014-12-04 04:47:28.445702144</td>\n      <td>2014-12-03 13:58:23.675396352</td>\n      <td>1.481799e+01</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2014-11-18 00:00:00</td>\n      <td>2014-11-18 00:00:00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2014-11-26 15:00:00</td>\n      <td>2014-11-26 00:00:00</td>\n      <td>1.000000e+01</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2014-12-04 14:00:00</td>\n      <td>2014-12-04 00:00:00</td>\n      <td>1.600000e+01</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2014-12-11 23:00:00</td>\n      <td>2014-12-11 00:00:00</td>\n      <td>2.000000e+01</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2014-12-18 23:00:00</td>\n      <td>2014-12-18 00:00:00</td>\n      <td>2.300000e+01</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.474778e+00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算所有列，各算各的\n",
    "data_user.describe(include = 'all')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:55:47.595165100Z",
     "start_time": "2024-05-06T09:55:33.797591600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1 计算每日的pv和uv\n",
    "### pv：页面访问量，uv：不同用户访问量"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "#计算pv，后面选任何一列即可，可以选item_id\n",
    "pv_daily = data_user.groupby('date').count()['user_id']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:57:15.505455Z",
     "start_time": "2024-05-06T09:57:12.122464500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": "date\n2014-11-18    366701\n2014-11-19    358823\n2014-11-20    353429\n2014-11-21    333104\n2014-11-22    361355\nName: user_id, dtype: int64"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#得到了每天的pv\n",
    "pv_daily.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:57:44.653834100Z",
     "start_time": "2024-05-06T09:57:44.570244600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "#如果不小心把名字弄错了，不需要重新读取数据，直接改列名\n",
    "#改为user_id1的目的是为了下面命名为pv,uv做准备\n",
    "pv_daily = pv_daily.rename('user_id1') #注意这里一定要写赋值"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T09:59:54.853036400Z",
     "start_time": "2024-05-06T09:59:54.825072200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "#不设置下面内容，避免显示崩溃\n",
    "#显示所有列\n",
    "pd.set_option('display.max_columns', 30)\n",
    "#显示所有行\n",
    "pd.set_option('display.max_rows', 100)\n",
    "#设置value的显示长度为100，默认为50\n",
    "pd.set_option('max_colwidth',100)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T10:00:07.776027700Z",
     "start_time": "2024-05-06T10:00:07.741518100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "0            73462715\n1            36090137\n2            40459733\n366701       81506172\n366702        1926899\n366703       51204443\n725524       35427753\n725525       73854016\n725526       78314284\n1078953      74952849\n1078954     139174569\n1078955      33596774\n1412057      97019627\n1412058     131112654\n1412059     134233776\n1773412      15541192\n1773413      89428650\n1773414      88090767\n2156114      59659116\n2156115     125482009\n2156116      59511789\n2534456     113693068\n2534457     126215864\n2534458      94992306\n2904695     114726208\n2904696      73196588\n2904697      45773960\n3265591      84441355\n3265592      48677702\n3265593      12570225\n3636975      64699430\n3636976      38387226\n3636977      33513319\n3977613      79134636\n3977614     135639209\n3977615      35272739\n4342310     124063835\n4342311      29689901\n4342312     103936681\n4743930      94943213\n4743931      10825402\n4743932      47693061\n5138541     112044266\n5138542     112044266\n5138543      72990071\n5543757       5837961\n5543758      21465106\n5543759      10309048\n5955363      80378029\n5955364     113285889\n5955365      80378029\n6355315      95290487\n6355316     138301582\n6355317     100637858\n6717193     126951542\n6717194     119675939\n6717195      66688297\n7106803     132627681\n7106804      91351115\n7106805      87351617\n7506554     120685346\n7506555      48942128\n7506556      47874120\n7893221     134479240\n7893222      28975138\n7893223       3318666\n8291246      64030719\n8291247      81840544\n8291248      65683052\n8713156     112747648\n8713157      78704357\n8713158     105278727\n9201664      43488815\n9201665      57190135\n9201666     110276286\n9893376      89886681\n9893377       8679255\n9893378     109927373\n10300536     12055347\n10300537    139145544\n10300538    118061360\n10703077     73218701\n10703078    133754248\n10703079    121000384\n11101433       100539\n11101434     10319860\n11101435     52895098\n11496518     33544881\n11496519     15158257\n11496520       189833\n11881309     52895098\n11881310     62360262\n11881311     37981108\nName: user_id, dtype: object"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#每组的前三个用户，没啥用\n",
    "data_user.groupby('date')['user_id'].head(3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T10:01:47.273804100Z",
     "start_time": "2024-05-06T10:01:46.034127Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "0            73462715\n1            36090137\n2            40459733\n3              814199\n4           113309982\n              ...    \n11881309     52895098\n11881310     62360262\n11881311     37981108\n11881312     33984927\n11881313    125912657\nName: user_id, Length: 155, dtype: object"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.groupby('date')['user_id'].head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-06T10:24:45.116191800Z",
     "start_time": "2024-05-06T10:24:44.063007400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "#如何计算uv，针对用户访问进行去重，再计算数目\n",
    "uv_daily = data_user.groupby('date')['user_id'].apply(lambda x:x.drop_duplicates().count())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-06T10:28:31.751597700Z",
     "start_time": "2024-05-06T10:28:29.737184200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    6343\n2014-11-19    6420\n2014-11-20    6333\n2014-11-21    6276\n2014-11-22    6187\nName: user_id, dtype: int64"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uv_daily.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T10:28:46.776259300Z",
     "start_time": "2024-05-06T10:28:46.733592Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id  item_id\ndate                        \n2014-11-18     6343   141604\n2014-11-19     6420   137984\n2014-11-20     6333   135314\n2014-11-21     6276   129629\n2014-11-22     6187   140171",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-11-18</th>\n      <td>6343</td>\n      <td>141604</td>\n    </tr>\n    <tr>\n      <th>2014-11-19</th>\n      <td>6420</td>\n      <td>137984</td>\n    </tr>\n    <tr>\n      <th>2014-11-20</th>\n      <td>6333</td>\n      <td>135314</td>\n    </tr>\n    <tr>\n      <th>2014-11-21</th>\n      <td>6276</td>\n      <td>129629</td>\n    </tr>\n    <tr>\n      <th>2014-11-22</th>\n      <td>6187</td>\n      <td>140171</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#另外一种计算uv的方法\n",
    "uv_daily1 = data_user.groupby('date').agg({'user_id':'nunique'})\n",
    "uv_daily1.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T13:27:26.338347800Z",
     "start_time": "2024-05-06T13:27:15.247299800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [],
   "source": [
    "#pv和uv结合在一起，变为df\n",
    "pv_uv_daily = pd.concat([pv_daily, uv_daily],axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T10:30:09.840685600Z",
     "start_time": "2024-05-06T10:30:09.823731600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "            user_id1  user_id\ndate                         \n2014-11-18    366701     6343\n2014-11-19    358823     6420\n2014-11-20    353429     6333\n2014-11-21    333104     6276\n2014-11-22    361355     6187",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id1</th>\n      <th>user_id</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-11-18</th>\n      <td>366701</td>\n      <td>6343</td>\n    </tr>\n    <tr>\n      <th>2014-11-19</th>\n      <td>358823</td>\n      <td>6420</td>\n    </tr>\n    <tr>\n      <th>2014-11-20</th>\n      <td>353429</td>\n      <td>6333</td>\n    </tr>\n    <tr>\n      <th>2014-11-21</th>\n      <td>333104</td>\n      <td>6276</td>\n    </tr>\n    <tr>\n      <th>2014-11-22</th>\n      <td>361355</td>\n      <td>6187</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_uv_daily.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T10:30:13.400743300Z",
     "start_time": "2024-05-06T10:30:13.379749Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [],
   "source": [
    "#改列名\n",
    "pv_uv_daily.rename(columns={'user_id1':'pv','user_id':'uv'},inplace =True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T10:30:24.764627500Z",
     "start_time": "2024-05-06T10:30:24.724194900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "                pv    uv\ndate                    \n2014-11-18  366701  6343\n2014-11-19  358823  6420\n2014-11-20  353429  6333\n2014-11-21  333104  6276\n2014-11-22  361355  6187",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pv</th>\n      <th>uv</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-11-18</th>\n      <td>366701</td>\n      <td>6343</td>\n    </tr>\n    <tr>\n      <th>2014-11-19</th>\n      <td>358823</td>\n      <td>6420</td>\n    </tr>\n    <tr>\n      <th>2014-11-20</th>\n      <td>353429</td>\n      <td>6333</td>\n    </tr>\n    <tr>\n      <th>2014-11-21</th>\n      <td>333104</td>\n      <td>6276</td>\n    </tr>\n    <tr>\n      <th>2014-11-22</th>\n      <td>361355</td>\n      <td>6187</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_uv_daily.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T10:30:26.828703300Z",
     "start_time": "2024-05-06T10:30:26.784910100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "#写入文件\n",
    "pv_uv_daily.to_csv('pv_uv.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T10:31:07.529330Z",
     "start_time": "2024-05-06T10:31:07.489643100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# pv_uv_daily.head(5).to_excel('pv_uv.xlsx')  #大家用这个，安装openpyxl\n",
    "pv_uv_daily.head(5).to_excel('pv_uv.xls')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#pv_uv_daily.corr(method = 'spearman')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#pv_uv_daily.corr(method = 'pearson')  #皮尔逊相关系数,到推荐那里讲"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#上面说明每天的访问量和访问用户是正相关的关系"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 画图得出结论：双12达到峰值"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x1000 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#默认显示一个月的区别\n",
    "plt.figure(figsize=(16,10))\n",
    "\n",
    "plt.subplot(211)  #等价于2,1,2，是2行1列图，这里是第一个图\n",
    "plt.plot(pv_daily, color='red')  #pv_daily是一个series，index是x，values是y\n",
    "plt.title('每天访问量')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(uv_daily,color='green')\n",
    "plt.title('每天访问用户数')\n",
    "\n",
    "plt.suptitle('UV和PV变化趋势')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T10:35:34.995937100Z",
     "start_time": "2024-05-06T10:35:34.242872Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 多维拆解，从小时维度观察pv和uv"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [],
   "source": [
    "#小时的pv\n",
    "pv_daily = data_user.groupby('hour').count()['user_id']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T11:41:23.128511300Z",
     "start_time": "2024-05-06T11:41:19.330698800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "data": {
      "text/plain": "hour\n0      517404\n1      267682\n2      147090\n3       98516\n4       80487\n5       88296\n6      158798\n7      287337\n8      396106\n9      485951\n10     550665\n11     526940\n12     531957\n13     598343\n14     594215\n15     598849\n16     576207\n17     505936\n18     547383\n19     735192\n20     935161\n21    1090178\n22    1088961\n23     849252\nName: user_id, dtype: int64"
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_daily"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T11:41:36.250395200Z",
     "start_time": "2024-05-06T11:41:36.226038800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [],
   "source": [
    "#小时的uv\n",
    "uv_daily = data_user.groupby('hour')['user_id'].apply(lambda x:x.drop_duplicates().count())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T11:50:05.140617100Z",
     "start_time": "2024-05-06T11:50:03.169392500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "hour\n0     5786\n1     3780\n2     2532\n3     1937\n4     1765\n5     2030\n6     3564\n7     5722\n8     7108\n9     7734\n10    8139\n11    8239\n12    8314\n13    8352\n14    8255\n15    8257\n16    8320\n17    8228\n18    8278\n19    8538\n20    8780\n21    8866\n22    8599\n23    7484\nName: user_id, dtype: int64"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uv_daily"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T11:50:06.885027900Z",
     "start_time": "2024-05-06T11:50:06.842057500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [],
   "source": [
    "pv_uv_daily = pd.concat([pv_daily, uv_daily],axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T11:52:05.531245700Z",
     "start_time": "2024-05-06T11:52:05.494637800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [],
   "source": [
    "pv_uv_daily.columns=['pv','uv'] #另外一种换索引名字的方式"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T11:52:16.663116500Z",
     "start_time": "2024-05-06T11:52:16.637159800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [
    {
     "data": {
      "text/plain": "           pv    uv\nhour               \n0      517404  5786\n1      267682  3780\n2      147090  2532\n3       98516  1937\n4       80487  1765\n5       88296  2030\n6      158798  3564\n7      287337  5722\n8      396106  7108\n9      485951  7734\n10     550665  8139\n11     526940  8239\n12     531957  8314\n13     598343  8352\n14     594215  8255\n15     598849  8257\n16     576207  8320\n17     505936  8228\n18     547383  8278\n19     735192  8538\n20     935161  8780\n21    1090178  8866\n22    1088961  8599\n23     849252  7484",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pv</th>\n      <th>uv</th>\n    </tr>\n    <tr>\n      <th>hour</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>517404</td>\n      <td>5786</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>267682</td>\n      <td>3780</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>147090</td>\n      <td>2532</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>98516</td>\n      <td>1937</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>80487</td>\n      <td>1765</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>88296</td>\n      <td>2030</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>158798</td>\n      <td>3564</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>287337</td>\n      <td>5722</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>396106</td>\n      <td>7108</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>485951</td>\n      <td>7734</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>550665</td>\n      <td>8139</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>526940</td>\n      <td>8239</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>531957</td>\n      <td>8314</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>598343</td>\n      <td>8352</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>594215</td>\n      <td>8255</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>598849</td>\n      <td>8257</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>576207</td>\n      <td>8320</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>505936</td>\n      <td>8228</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>547383</td>\n      <td>8278</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>735192</td>\n      <td>8538</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>935161</td>\n      <td>8780</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>1090178</td>\n      <td>8866</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>1088961</td>\n      <td>8599</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>849252</td>\n      <td>7484</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_uv_daily"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T11:52:27.292516600Z",
     "start_time": "2024-05-06T11:52:27.244606Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "21\n"
     ]
    }
   ],
   "source": [
    "# 最大的pv、uv都是21点\n",
    "print(pv_daily.idxmax())\n",
    "print(uv_daily.idxmax())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T11:54:35.346196800Z",
     "start_time": "2024-05-06T11:54:35.333907100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#pv_uv_daily.corr(method = 'spearman')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#pv_uv_daily.corr(method = 'pearson')  #皮尔逊相关系数，看pv和uv相关性"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 画图得出结论：每天晚上的9、10点是购物高峰"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "pandas提供了一个非常方便的接口，允许你直接对DataFrame的列进行操作，包括绘图。\n",
    "\n",
    "当你使用pv_uv_daily['pv'].plot()这样的语法时，实际上是调用了pandas库为Series对象提供的一个特殊方法.plot()。这个方法是matplotlib绘图方法的一个封装，它允许你直接对pandas对象进行绘图，而不需要显式地调用matplotlib的函数。\n",
    "\n",
    "这样做的好处包括：\n",
    "\n",
    "简洁性：你不需要从DataFrame中提取数据，然后再传递给matplotlib函数，可以直接在DataFrame的列上调用.plot()方法。\n",
    "\n",
    "自动化：pandas会自动处理Series的索引作为x轴的数据，而Series的值作为y轴的数据。\n",
    "\n",
    "一致性：pandas的.plot()方法提供了一种一致的方式来绘制不同的Series或DataFrame，无需改变代码结构。\n",
    "\n",
    "功能集成：pandas的.plot()方法还提供了一些额外的功能，比如自动的图例生成，以及对时间序列数据的内置支持。"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x900 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "\n",
    "#plt.plot(pv_uv_daily['pv'], color='steelblue',label='每个小时访问量')\n",
    "pv_uv_daily['pv'].plot(color= 'steelblue', label ='每个小时访问量')\n",
    "plt.legend(loc ='upper center')\n",
    "plt.ylabel('访问量')\n",
    "\n",
    "# 创建一个辅助轴用于UV曲线\n",
    "# ax2 = plt.twinx()  # 创建一个共享x轴的辅助y轴\n",
    "# # 绘制UV曲线，使用辅助轴\n",
    "# ax2.plot(pv_uv_daily['uv'], color='red', label='每个小时不同用户访问量')\n",
    "# ax2.set_ylabel('访问用户数')  # 设置UV曲线的y轴标签（辅助轴）\n",
    "\n",
    "pv_uv_daily['uv'].plot(color= 'red', label='每个小时不同用户访问量',secondary_y =True)\n",
    "plt.ylabel('访问用户数')\n",
    "plt.xticks(range(0,24),pv_uv_daily.index)\n",
    "plt.legend(loc ='upper center')\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T12:05:49.318144500Z",
     "start_time": "2024-05-06T12:05:48.398606300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 多维拆解，从小时的维度，看不同的行为，点击、收藏、加入购物车和支付"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "outputs": [
    {
     "data": {
      "text/plain": "     user_id    item_id behavior_type user_geohash item_category       time  \\\n0   73462715  378485233             1          NaN          9130 2014-11-18   \n1   36090137  236748115             1          NaN         10523 2014-11-18   \n2   40459733  155218177             1          NaN          8561 2014-11-18   \n3     814199  149808524             1          NaN          9053 2014-11-18   \n4  113309982    5730861             1          NaN          3783 2014-11-18   \n\n        date  hour  \n0 2014-11-18     0  \n1 2014-11-18     0  \n2 2014-11-18     0  \n3 2014-11-18     0  \n4 2014-11-18     0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user_id</th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>date</th>\n      <th>hour</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>73462715</td>\n      <td>378485233</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9130</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>36090137</td>\n      <td>236748115</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>10523</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>40459733</td>\n      <td>155218177</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>8561</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>814199</td>\n      <td>149808524</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>9053</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>113309982</td>\n      <td>5730861</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>3783</td>\n      <td>2014-11-18</td>\n      <td>2014-11-18</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T14:17:15.449876600Z",
     "start_time": "2024-05-06T14:17:15.395952600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "pivot_table()用于创建一个数据透视表\n",
    "\n",
    "columns='behavior_type'：这个参数指定了数据透视表中的列。behavior_type是data_user DataFrame中的一个列名，它将被用作数据透视表中的列分组依据。\n",
    "\n",
    "index='hour'：这个参数指定了数据透视表中的行索引。hour是data_user DataFrame中的一个列名，它将被用作数据透视表中的行索引。\n",
    "\n",
    "data=data_user：这是要进行数据透视操作的原始数据集，即一个pandas.DataFrame对象。\n",
    "\n",
    "values='item_id'：这个参数指定了要聚合的值。item_id是data_user DataFrame中的一个列名，它包含了需要聚合的数据。\n",
    "\n",
    "aggfunc=np.size：这是一个聚合函数，用于对values参数指定的列中的值进行聚合。np.size是NumPy库中的一个函数，它返回数组元素的总数。在这个上下文中，它将计算每个behavior_type和hour组合中item_id的数量。"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "outputs": [],
   "source": [
    "# 对于每个hour和behavior_type的组合，计算item_id的唯一数量。\n",
    "pv_detail = pd.pivot_table(columns ='behavior_type',index ='hour' ,\n",
    "                           data = data_user,\n",
    "                           values = 'item_id',aggfunc=np.size)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T14:18:15.161090200Z",
     "start_time": "2024-05-06T14:18:06.427672Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "outputs": [
    {
     "data": {
      "text/plain": "behavior_type        1      2      3     4\nhour                                      \n0               487341  11062  14156  4845\n1               252991   6276   6712  1703\n2               139139   3311   3834   806\n3                93250   2282   2480   504\n4                75832   2010   2248   397\n5                83545   2062   2213   476\n6               150356   3651   3768  1023\n7               272470   5885   7044  1938\n8               374701   7849   9970  3586\n9               456781  10507  12956  5707\n10              515960  11185  16203  7317\n11              493679  10918  15257  7086\n12              500036   9940  15025  6956\n13              561513  11694  17419  7717\n14              558246  11695  17067  7207\n15              562238  12010  17289  7312\n16              541846  11127  16304  6930\n17              476369   9754  14515  5298\n18              517078  10342  14823  5140\n19              696035  13952  18853  6352\n20              885669  16599  25021  7872\n21             1030483  20397  30469  8829\n22             1027269  20343  32504  8845\n23              797754  17705  27434  6359",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>behavior_type</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n    </tr>\n    <tr>\n      <th>hour</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>487341</td>\n      <td>11062</td>\n      <td>14156</td>\n      <td>4845</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>252991</td>\n      <td>6276</td>\n      <td>6712</td>\n      <td>1703</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>139139</td>\n      <td>3311</td>\n      <td>3834</td>\n      <td>806</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>93250</td>\n      <td>2282</td>\n      <td>2480</td>\n      <td>504</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>75832</td>\n      <td>2010</td>\n      <td>2248</td>\n      <td>397</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>83545</td>\n      <td>2062</td>\n      <td>2213</td>\n      <td>476</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>150356</td>\n      <td>3651</td>\n      <td>3768</td>\n      <td>1023</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>272470</td>\n      <td>5885</td>\n      <td>7044</td>\n      <td>1938</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>374701</td>\n      <td>7849</td>\n      <td>9970</td>\n      <td>3586</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>456781</td>\n      <td>10507</td>\n      <td>12956</td>\n      <td>5707</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>515960</td>\n      <td>11185</td>\n      <td>16203</td>\n      <td>7317</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>493679</td>\n      <td>10918</td>\n      <td>15257</td>\n      <td>7086</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>500036</td>\n      <td>9940</td>\n      <td>15025</td>\n      <td>6956</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>561513</td>\n      <td>11694</td>\n      <td>17419</td>\n      <td>7717</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>558246</td>\n      <td>11695</td>\n      <td>17067</td>\n      <td>7207</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>562238</td>\n      <td>12010</td>\n      <td>17289</td>\n      <td>7312</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>541846</td>\n      <td>11127</td>\n      <td>16304</td>\n      <td>6930</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>476369</td>\n      <td>9754</td>\n      <td>14515</td>\n      <td>5298</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>517078</td>\n      <td>10342</td>\n      <td>14823</td>\n      <td>5140</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>696035</td>\n      <td>13952</td>\n      <td>18853</td>\n      <td>6352</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>885669</td>\n      <td>16599</td>\n      <td>25021</td>\n      <td>7872</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>1030483</td>\n      <td>20397</td>\n      <td>30469</td>\n      <td>8829</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>1027269</td>\n      <td>20343</td>\n      <td>32504</td>\n      <td>8845</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>797754</td>\n      <td>17705</td>\n      <td>27434</td>\n      <td>6359</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_detail #点击、收藏、加入购物车和支付 的比例"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T14:18:23.062954700Z",
     "start_time": "2024-05-06T14:18:23.008671400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x900 with 1 Axes>",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.lineplot(data = pv_detail)\n",
    "# 使用seaborn的lineplot函数来绘制线图。\n",
    "# 这里传递给lineplot的data参数是pv_detail数据透视表的子集，由iloc[:,1:]指定\n",
    "# sns.lineplot会自动处理多条线的情况，\n",
    "# 如果pv_detail的列（除了索引列）代表不同的类别或组，那么lineplot将为每个类别绘制一条线。\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T12:37:21.197861200Z",
     "start_time": "2024-05-06T12:37:20.877714200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x900 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.lineplot(data = pv_detail.iloc[:,1:])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T12:37:23.905768800Z",
     "start_time": "2024-05-06T12:37:23.603673200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# ARPU和ARPPU\n",
    "### ARPU= 总收入/用户总数\n",
    "### ARPPU= 总收入/付费用户总数"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "outputs": [],
   "source": [
    "data_user_buy = data_user[data_user.behavior_type =='4'].groupby('user_id').size()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T12:49:14.036878300Z",
     "start_time": "2024-05-06T12:49:12.908893200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "outputs": [
    {
     "data": {
      "text/plain": "user_id\n100001878    36\n100011562     3\n100012968    15\n100014060    24\n100024529    26\n             ..\n99960313      8\n9996155       6\n99963140     19\n99968428     38\n99989881     17\nLength: 8886, dtype: int64"
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy  #每个用户的购买次数"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T14:26:53.601247400Z",
     "start_time": "2024-05-06T14:26:53.551512400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 120205 entries, 103 to 12256879\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count   Dtype         \n",
      "---  ------         --------------   -----         \n",
      " 0   user_id        120205 non-null  object        \n",
      " 1   item_id        120205 non-null  object        \n",
      " 2   behavior_type  120205 non-null  object        \n",
      " 3   user_geohash   38608 non-null   object        \n",
      " 4   item_category  120205 non-null  object        \n",
      " 5   time           120205 non-null  datetime64[ns]\n",
      " 6   date           120205 non-null  datetime64[ns]\n",
      " 7   hour           120205 non-null  int8          \n",
      "dtypes: datetime64[ns](2), int8(1), object(5)\n",
      "memory usage: 7.5+ MB\n",
      "None\n",
      "120205\n"
     ]
    }
   ],
   "source": [
    "print(data_user[data_user.behavior_type =='4'].info())    # 120205次购买\n",
    "print(np.sum(data_user_buy.values))  # 两种计算方法"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-06T13:17:55.843958300Z",
     "start_time": "2024-05-06T13:17:54.801948200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "outputs": [
    {
     "data": {
      "text/plain": "count    8886.000000\nmean       13.527459\nstd        19.698786\nmin         1.000000\n25%         4.000000\n50%         8.000000\n75%        17.000000\nmax       809.000000\ndtype: float64"
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy.describe()  #可以知道大部分用户一个月的购买次数是8次"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T12:51:51.739828600Z",
     "start_time": "2024-05-06T12:51:51.684981200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x =data_user_buy, bins=30)\n",
    "plt.show()\n",
    "#通过图形发现大部分人的购买次数都小于20次，做一下处理，再画直方图"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T12:51:54.204645400Z",
     "start_time": "2024-05-06T12:51:53.956908600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x =data_user_buy[data_user_buy<=20], bins=20)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T12:51:59.144211700Z",
     "start_time": "2024-05-06T12:51:58.926793100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "outputs": [
    {
     "data": {
      "text/plain": "601.025"
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy.sum()*50/10000  #ARPU值\n",
    "# data_user_buy.sum()是每个用户购买次数之和，这里假设每次消费50元。10000是总用户数，之前算出来了"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T13:18:35.620102400Z",
     "start_time": "2024-05-06T13:18:35.593136800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 下面计算日ARPPU"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "outputs": [],
   "source": [
    "data_user_buy1= data_user[data_user.behavior_type =='4' ].groupby(['date' , 'user_id']).count()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T13:20:23.666005400Z",
     "start_time": "2024-05-06T13:20:22.601942300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "outputs": [
    {
     "data": {
      "text/plain": "                      item_id  behavior_type  user_geohash  item_category  \\\ndate       user_id                                                          \n2014-11-18 100001878        1              1             1              1   \n           100014060        2              2             2              2   \n           100024529        6              6             0              6   \n           100027681        3              3             0              3   \n           10004287         2              2             0              2   \n\n                      time  hour  \ndate       user_id                \n2014-11-18 100001878     1     1  \n           100014060     2     2  \n           100024529     6     6  \n           100027681     3     3  \n           10004287      2     2  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>item_id</th>\n      <th>behavior_type</th>\n      <th>user_geohash</th>\n      <th>item_category</th>\n      <th>time</th>\n      <th>hour</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th>user_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">2014-11-18</th>\n      <th>100001878</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>100014060</th>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>100024529</th>\n      <td>6</td>\n      <td>6</td>\n      <td>0</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>100027681</th>\n      <td>3</td>\n      <td>3</td>\n      <td>0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>10004287</th>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T13:20:24.656171500Z",
     "start_time": "2024-05-06T13:20:24.591542600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "outputs": [
    {
     "data": {
      "text/plain": "        date    user_id   total\n0 2014-11-18  100001878       1\n1 2014-11-18  100014060       2\n2 2014-11-18  100024529       6\n3 2014-11-18  100027681       3\n4 2014-11-18   10004287       2",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>user_id</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014-11-18</td>\n      <td>100001878</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014-11-18</td>\n      <td>100014060</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014-11-18</td>\n      <td>100024529</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014-11-18</td>\n      <td>100027681</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014-11-18</td>\n      <td>10004287</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # 不同日期、不同用户的购买行为次数\n",
    "data_user_buy1['behavior_type'].reset_index().rename(columns={'behavior_type':' total'}).head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T13:20:42.236864400Z",
     "start_time": "2024-05-06T13:20:42.127243700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "outputs": [],
   "source": [
    "# 按照日期不同用户的购买行为次数\n",
    "# 把上面两步二和一\n",
    "data_user_buy1= data_user[data_user.behavior_type =='4' ].groupby(['date' , 'user_id']).\\\n",
    "count()['behavior_type'].reset_index().rename(columns={'behavior_type':'total'})"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T14:32:48.764058700Z",
     "start_time": "2024-05-06T14:32:46.739980Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "outputs": [
    {
     "data": {
      "text/plain": "        date    user_id  total\n0 2014-11-18  100001878      1\n1 2014-11-18  100014060      2\n2 2014-11-18  100024529      6\n3 2014-11-18  100027681      3\n4 2014-11-18   10004287      2",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>user_id</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014-11-18</td>\n      <td>100001878</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014-11-18</td>\n      <td>100014060</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014-11-18</td>\n      <td>100024529</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014-11-18</td>\n      <td>100027681</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014-11-18</td>\n      <td>10004287</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T14:32:49.935882100Z",
     "start_time": "2024-05-06T14:32:49.848116100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "outputs": [
    {
     "data": {
      "text/plain": "                                date         total\ncount                          49201  49201.000000\nmean   2014-12-03 14:07:33.795654400      2.443141\nmin              2014-11-18 00:00:00      1.000000\n25%              2014-11-26 00:00:00      1.000000\n50%              2014-12-04 00:00:00      1.000000\n75%              2014-12-12 00:00:00      3.000000\nmax              2014-12-18 00:00:00    185.000000\nstd                              NaN      3.307288",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>49201</td>\n      <td>49201.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2014-12-03 14:07:33.795654400</td>\n      <td>2.443141</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2014-11-18 00:00:00</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2014-11-26 00:00:00</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2014-12-04 00:00:00</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2014-12-12 00:00:00</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2014-12-18 00:00:00</td>\n      <td>185.000000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>NaN</td>\n      <td>3.307288</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T14:41:50.236566700Z",
     "start_time": "2024-05-06T14:41:50.150529Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18     3730\n2014-11-19     3686\n2014-11-20     3462\n2014-11-21     3021\n2014-11-22     3570\n2014-11-23     3347\n2014-11-24     3426\n2014-11-25     3464\n2014-11-26     3573\n2014-11-27     3670\n2014-11-28     3218\n2014-11-29     3211\n2014-11-30     3616\n2014-12-01     3909\n2014-12-02     3621\n2014-12-03     3885\n2014-12-04     3691\n2014-12-05     3319\n2014-12-06     3272\n2014-12-07     3256\n2014-12-08     3419\n2014-12-09     3449\n2014-12-10     3216\n2014-12-11     3226\n2014-12-12    15251\n2014-12-13     3478\n2014-12-14     3483\n2014-12-15     3764\n2014-12-16     3771\n2014-12-17     3615\n2014-12-18     3586\nName: total, dtype: int64"
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.groupby('date').sum()['total']   # 每天的购买行为次数之和"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-06T14:42:16.560868900Z",
     "start_time": "2024-05-06T14:42:16.488016900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    1539\n2014-11-19    1511\n2014-11-20    1492\n2014-11-21    1330\n2014-11-22    1411\n2014-11-23    1436\n2014-11-24    1524\n2014-11-25    1497\n2014-11-26    1487\n2014-11-27    1527\n2014-11-28    1442\n2014-11-29    1377\n2014-11-30    1534\n2014-12-01    1657\n2014-12-02    1585\n2014-12-03    1697\n2014-12-04    1585\n2014-12-05    1493\n2014-12-06    1452\n2014-12-07    1403\n2014-12-08    1551\n2014-12-09    1429\n2014-12-10    1442\n2014-12-11    1449\n2014-12-12    3897\n2014-12-13    1549\n2014-12-14    1506\n2014-12-15    1627\n2014-12-16    1650\n2014-12-17    1570\n2014-12-18    1552\nName: total, dtype: int64"
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.groupby('date').count()['total']  # 每天的购买用户数"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-06T14:42:41.282454300Z",
     "start_time": "2024-05-06T14:42:41.212965500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "outputs": [],
   "source": [
    "# 每个付费用户，平均每天的支付次数\n",
    "data_user_buy2=data_user_buy1.groupby('date').sum()['total']/data_user_buy1.groupby('date').count()['total']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T14:44:00.444575600Z",
     "start_time": "2024-05-06T14:44:00.385764500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    2.423652\n2014-11-19    2.439444\n2014-11-20    2.320375\n2014-11-21    2.271429\n2014-11-22    2.530120\n2014-11-23    2.330780\n2014-11-24    2.248031\n2014-11-25    2.313961\n2014-11-26    2.402824\n2014-11-27    2.403405\n2014-11-28    2.231623\n2014-11-29    2.331881\n2014-11-30    2.357236\n2014-12-01    2.359083\n2014-12-02    2.284543\n2014-12-03    2.289334\n2014-12-04    2.328707\n2014-12-05    2.223041\n2014-12-06    2.253444\n2014-12-07    2.320741\n2014-12-08    2.204384\n2014-12-09    2.413576\n2014-12-10    2.230236\n2014-12-11    2.226363\n2014-12-12    3.913523\n2014-12-13    2.245320\n2014-12-14    2.312749\n2014-12-15    2.313460\n2014-12-16    2.285455\n2014-12-17    2.302548\n2014-12-18    2.310567\nName: total, dtype: float64"
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T14:44:02.974652700Z",
     "start_time": "2024-05-06T14:44:02.912156700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "outputs": [
    {
     "data": {
      "text/plain": "date\n2014-11-18    2.423652\n2014-11-19    2.439444\n2014-11-20    2.320375\n2014-11-21    2.271429\n2014-11-22    2.530120\n2014-11-23    2.330780\n2014-11-24    2.248031\n2014-11-25    2.313961\n2014-11-26    2.402824\n2014-11-27    2.403405\n2014-11-28    2.231623\n2014-11-29    2.331881\n2014-11-30    2.357236\n2014-12-01    2.359083\n2014-12-02    2.284543\n2014-12-03    2.289334\n2014-12-04    2.328707\n2014-12-05    2.223041\n2014-12-06    2.253444\n2014-12-07    2.320741\n2014-12-08    2.204384\n2014-12-09    2.413576\n2014-12-10    2.230236\n2014-12-11    2.226363\n2014-12-12    3.913523\n2014-12-13    2.245320\n2014-12-14    2.312749\n2014-12-15    2.313460\n2014-12-16    2.285455\n2014-12-17    2.302548\n2014-12-18    2.310567\nName: total, dtype: float64"
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy1.groupby('date').mean()['total']  #另外一种计算方法"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T13:33:56.158377900Z",
     "start_time": "2024-05-06T13:33:56.102527700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2014-11-18', '2014-11-19', '2014-11-20', '2014-11-21',\n               '2014-11-22', '2014-11-23', '2014-11-24', '2014-11-25',\n               '2014-11-26', '2014-11-27', '2014-11-28', '2014-11-29',\n               '2014-11-30', '2014-12-01', '2014-12-02', '2014-12-03',\n               '2014-12-04', '2014-12-05', '2014-12-06', '2014-12-07',\n               '2014-12-08', '2014-12-09', '2014-12-10', '2014-12-11',\n               '2014-12-12', '2014-12-13', '2014-12-14', '2014-12-15',\n               '2014-12-16', '2014-12-17', '2014-12-18'],\n              dtype='datetime64[ns]', name='date', freq=None)"
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy2.index"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-06T13:39:56.796670300Z",
     "start_time": "2024-05-06T13:39:56.778717500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x900 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,9))\n",
    "data_user_buy2.plot()\n",
    "plt.xticks(data_user_buy2.index[::3],data_user_buy2.index[::3],rotation=45) # 11月18号到12月18号\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T13:40:35.171047700Z",
     "start_time": "2024-05-06T13:40:34.809962800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "outputs": [
    {
     "data": {
      "text/plain": "count    31.000000\nmean      2.368446\nstd       0.296108\nmin       2.204384\n25%       2.262436\n50%       2.313460\n75%       2.358159\nmax       3.913523\nName: total, dtype: float64"
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_user_buy2.describe()   #发现每天大家的平均购物次数是2.3次，双12当天达到最高3.9次"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T14:44:23.442233600Z",
     "start_time": "2024-05-06T14:44:23.380506100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 这里只计算了日均购买次数，如果要计算日ARPPU，还需要乘以50元（假设每次消费50元）"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-06T13:51:48.528752800Z",
     "start_time": "2024-05-06T13:51:48.479380600Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
